SubTB(\(\lambda \) ) draws inspiration from the TD(\(\lambda \) ) algorithm in RL [1]}, [2]}, which forms an estimate of the expected return via a convex combination of \(n\) -step returns, each weighed by \((1-\lambda )\lambda ^{n-1}\) . The parameter \(\lambda \in [0,1]\)  enables a bias-variance tradeoff [3]}. Intuitively, larger \(\lambda \)  leads to lower bias and higher variance, since the estimate of the expected return approaches the single-point Monte Carlo estimate as \(\lambda \rightarrow 1\) . We take inspiration from this idea to mix together different (possibly all) subtrajectories, akin to how \(n\) -step returns are mixed together. We hypothesize that the right mixing may reduce variance, compared to TB, with the additional benefits of inducing consistency between the flows of intermediate states, and thus of helping propagate credit faster and enable faster convergence. In addition, GFlowNet training objectives are reminiscent of residual gradient RL methods [4]}, [5]} since the “endpoint” (e.g. \(F(s_n)\)  in (REF )) is also considered in the gradient.
